Architectural Innovation. The first category of work mainly focused on developing novel networks with powerful representation abilities. Some early studies encoded each image as a single feature vector through a pre-trained convolutional neural network (CNN), and then fed it to recurrent neural networks (RNNs) based on long short-term memory units [1]}, [2]}. Afterward, Xu et al. xushow2015 explored the attention mechanisms for image captioning, which could dynamically focus on salient features at each decoding step. Another important line of work cooperated the task "object detection" for image captioning [3]}, [4]}. Anderson et al. DBLP:conf/cvpr/00010BT0GZ18 extracted object-level features by a pre-trained Faster R-CNN model, and combined them with the attention mechanism, which achieved significantly higher performance than object-free models. Follow-up researches further captured the relationships among objects by adding self-attention on object-level features [5]}, or using graph convolutional networks [6]}, [7]}. More recently, Transformer-based [8]} models were proposed with spatial position embeddings and better normalization layers [9]}, meshed attentions [10]} or higher-order attentions [11]}. It is worthwhile to mention that almost all the state-of-the-art (SOTA) models utilized the self-critical reinforcement (SCRL) strategy [12]}.
